Identifying text in photos and images is an important feature in mobile augmented reality applications. However, identifying text in an image can be complex and may involve multiple subtasks, including text detection, text recognition, and application-specific post-processing and information retrieval. Conventional approaches are to operate various text identification subtasks in an open-loop pipeline.
However, images or videos captured by mobile devices often have poor qualities because of motion and focus blur, light variations or noise, and/or limitations associated with built-in cameras. Conventional approaches in text identification are not ideal for these types of low quality images or videos. Improvements to the conventional approaches are desired.